Speaker
Description
Interferometric observations of the Sunyaev-Zeldovich (SZ) effect provide critical insights into galaxy clusters, yet extracting the diffuse, negative SZ signal from visibility data remains a profound inverse problem. Traditional imaging algorithms struggle with these signal properties, while standard forward modelling is inherently limited by strong parametric assumptions. In this talk, I will present a paradigm shift: leveraging Deep Generative Models as robust, data-driven priors within a strict Bayesian framework.
By sampling the latent space of generative architectures, we perform inference where the ML model handles the complex, non-linear physics of the source, while the exact instrumental response is maintained as an explicit likelihood. I will compare the current state-of-the-art in generative imaging—Generative Adversarial Networks (GANs), Flow
Matching, and Diffusion Models—evaluating them not just on morphological realism, but on physical accuracy through Train on Synthetic, Test on Real (TSTR) conditioning tests and morphological comparisons with the training set. I will also try to address many challenges a researcher faces when training these kinds of models, discussing the trade-offs between computational cost, model stability, and the severe risk of memorization (overfitting) in data-hungry diffusion architectures.